In [1]:
import numpy as np
import pandas as pd

Readin CSV filr¶

In [2]:
df=pd.read_csv("india.csv")
In [3]:
df.head()
Out[3]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
3 Andhra Pradesh 31-08-2019 Monthly 3.32 12285693.0 43.97 Rural
4 Andhra Pradesh 30-09-2019 Monthly 5.17 12256762.0 44.68 Rural
In [4]:
df.tail()
Out[4]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
763 NaN NaN NaN NaN NaN NaN NaN
764 NaN NaN NaN NaN NaN NaN NaN
765 NaN NaN NaN NaN NaN NaN NaN
766 NaN NaN NaN NaN NaN NaN NaN
767 NaN NaN NaN NaN NaN NaN NaN
In [5]:
df.shape
Out[5]:
(768, 7)
In [6]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 7 columns):
 #   Column                                    Non-Null Count  Dtype  
---  ------                                    --------------  -----  
 0   Region                                    740 non-null    object 
 1    Date                                     740 non-null    object 
 2    Frequency                                740 non-null    object 
 3    Estimated Unemployment Rate (%)          740 non-null    float64
 4    Estimated Employed                       740 non-null    float64
 5    Estimated Labour Participation Rate (%)  740 non-null    float64
 6   Area                                      740 non-null    object 
dtypes: float64(3), object(4)
memory usage: 42.1+ KB
In [7]:
df.describe()
Out[7]:
Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%)
count 740.000000 7.400000e+02 740.000000
mean 11.787946 7.204460e+06 42.630122
std 10.721298 8.087988e+06 8.111094
min 0.000000 4.942000e+04 13.330000
25% 4.657500 1.190404e+06 38.062500
50% 8.350000 4.744178e+06 41.160000
75% 15.887500 1.127549e+07 45.505000
max 76.740000 4.577751e+07 72.570000
In [8]:
x= df['Region']
In [9]:
x
Out[9]:
0      Andhra Pradesh
1      Andhra Pradesh
2      Andhra Pradesh
3      Andhra Pradesh
4      Andhra Pradesh
            ...      
763               NaN
764               NaN
765               NaN
766               NaN
767               NaN
Name: Region, Length: 768, dtype: object
In [10]:
y=df[' Estimated Unemployment Rate (%)']
In [11]:
y
Out[11]:
0      3.65
1      3.05
2      3.75
3      3.32
4      5.17
       ... 
763     NaN
764     NaN
765     NaN
766     NaN
767     NaN
Name:  Estimated Unemployment Rate (%), Length: 768, dtype: float64
In [12]:
df2=df.iloc[:,3]
In [13]:
df2
Out[13]:
0      3.65
1      3.05
2      3.75
3      3.32
4      5.17
       ... 
763     NaN
764     NaN
765     NaN
766     NaN
767     NaN
Name:  Estimated Unemployment Rate (%), Length: 768, dtype: float64
In [14]:
import plotly.express as px
import matplotlib as plt
In [15]:
fg = px.bar(df,x='Region' ,y=' Estimated Unemployment Rate (%)' ,color='Region' ,title='Unemployment Rate(State Wise) by Bar Graph', template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [16]:
fg= px.bar(df,x='Region' ,y=' Estimated Unemployment Rate (%)' ,color='Region'  ,title='Unemployment Rate (State Wise) by Bar Graph' ,template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [17]:
fg = px.box(df ,x='Region' ,y=' Estimated Unemployment Rate (%)' ,color='Region' ,title='Unemployment Rate (Statewise) by Box Plot' ,template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [18]:
fg = px.scatter(df,x='Region' ,y=' Estimated Unemployment Rate (%)' ,color='Region' ,title='Unemployment Rate (Statewise) by Scatter Plot' ,template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [19]:
fg = px.histogram(df,x='Region' ,y=' Estimated Unemployment Rate (%)' ,color='Region' ,title='Unemployment Rate (Statewise) by Histogram' ,template='plotly')
fg.update_layout(xaxis={'categoryorder':'total descending'})
fg.show()
In [ ]: